Advertisement

A Grid-Map-Oriented UAV Flight Path Planning Algorithm Based on ACO Algorithm

  • Wei Tian
  • Zhihua YangEmail author
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 516)

Abstract

With the extensive applications of unmanned aerial vehicle (UAV), typical algorithm for path planning is usually restricted for its low efficiency and easy failure, especially for the complex obstacle environments. Therefore, in this paper, a new UAV path planning algorithm is proposed based on ant colony optimization (ACO) for such complex obstacle environment. In particular, the proposed algorithm optimizes the distribution of pheromones and modifies the transfer probability by considering the regional security factors. As a result, it can increase search speed and avoid local optimum and deadlock. Simulation results verify the feasibility and effectiveness of the proposed method.

Keywords

UAV Path planning Grid map ACO 

Notes

Acknowledgements

The authors would like to express their high appreciations to the supports from the Shenzhen Basic Research Project (JCYJ20150403161923521, JCYJ20170413110004682 and JCYJ20150403161923521).

References

  1. 1.
    Khatib O. Real-time obstacle avoidance for manipulators and mobile robots. Int J Robot Res. 1986;5(1):90–8.Google Scholar
  2. 2.
    Weerakoon T, Ishii K, Nassiraei AAF. An artificial potential field based mobile robot navigation method to prevent From Deadlock. J Artif Intell Soft Comput Res. 2015;5(3):189–203.CrossRefGoogle Scholar
  3. 3.
    Lazarowska A. Multi-criteria trajectory base path planning algorithm for a moving object in a dynamic environment. IEEE international conference on innovations in intelligent systems and applications. IEEE; 2017. p. 79–83.Google Scholar
  4. 4.
    Lin S. Computer solutions of the traveling salesman problem. Bell Labs Tech J. 2014;44(10):2245–69.MathSciNetCrossRefGoogle Scholar
  5. 5.
    Zhu QB, Zhang YL. An ant colony algorithm based on grid method for mobile robot path planning. Robot. 2005;27(2):132–6. Google Scholar
  6. 6.
    Dorigo M, Birattari M, Stutzle T. Ant colony optimization. IEEE Comput Intell Mag. 2007;1(4):28–39.CrossRefGoogle Scholar
  7. 7.
    Yuan M, Wang S, Li P. A model of ant colony and immune network and its application in path planning. In: IEEE conference on industrial electronics and applications. IEEE; 2008. p. 102–7.Google Scholar
  8. 8.
    Hu Y, Li D, Ding Y. A path planning algorithm based on genetic and ant colony dynamic integration. In: Intelligent control and automation. IEEE; 2015. p. 4881–6.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Communications Engineering Research CenterShenzhen Graduate School, Harbin Institute of TechnologyShenzhenChina

Personalised recommendations